Medical diagnostic supporting apparatus

ABSTRACT

A medical diagnostic supporting apparatus inputs a medical image to be a target of a medical diagnosis, acquires one or more pieces of medical information relating to the medical image as entered information, and acquires an image feature amount from the medical image. The medical diagnostic supporting apparatus selects a plurality of pieces of not-entered information associated with the acquired image feature amount from not-entered information that is medical information other than the entered information as presented not-entered information candidates that are candidates for presentation, and selects presented not-entered information from the presented not-entered information candidates based on a plurality of inference results acquired using the entered information and each of the presented not-entered information candidates. The medical diagnostic supporting apparatus presents the selected presented not-entered information to a doctor.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a technique and a system for providingcomputerized information which supports a medical diagnosis.

2. Description of the Related Art

In the medical field, a medical practitioner (e.g., a doctor) observesthe state of a lesioned part and a temporal change in the lesioned partby displaying a medical image obtained by capturing an image of apatient (or part thereof) on a monitor and interpreting the medicalimage displayed on the monitor. Examples of apparatuses generating suchkind of medical images include an X-ray computed tomography (CT)apparatus, a magnetic resonance imaging (MRI) apparatus, and anultrasonic apparatus.

A diagnosis (image diagnosis) using these medical images can be dividedinto the process of discovering, for example, an abnormal shadow from amedical image and obtaining the characteristics thereof, and the processof conducting a differential diagnosis to determine what the shadow is.

Conventionally, with the aim of supporting a differential diagnosis by adoctor, there has been developed a medical diagnostic supportingapparatus that infers and provides what an abnormal shadow is, based onentered information such as the characteristics of the abnormal shadow(observation from image interpretation). For example, there has beenproposed an apparatus for calculating the probability that a certainshadow in a chest X-ray CT image indicates a malignant tumor and theprobability that the shadow indicates a benign tumor, and presentingthem.

Normally, the appropriate procedure for using such an apparatus in anactual clinical practice is as follows; first, a doctor conducts adifferential diagnosis, and after that, the doctor refers to theinference result output from the medical diagnostic supporting apparatusas reference information.

One issue arising in this case is that, if there are a number of piecesof information that are not entered, the accuracy of inference by themedical diagnostic supporting apparatus is reduced. Therefore, anattempt has been made to obtain a more reliable inference result byconfiguring the apparatus to select not-entered information required foran inference and prompt a doctor to additionally enter that information.

Japanese Patent No. 3226400 discusses a technique of selecting andpresenting noteworthy not-entered information from an inference result(current inference result) of the apparatus based on information thathas been entered (entered information) and an inference result thatwould be produced if the information that is currently not entered isadded to the entered information. This technique calculates a degree ofinfluence that each not-entered information has on the current inferenceresult, and presents not-entered information that has a high influencedegree.

Further, Japanese Patent No. 3226400 discusses, as a method ofcalculating a degree of influence, the method of focusing the diagnosisname that has the highest probability in the current inference result,and using a change amount of the probability when not-enteredinformation is added thereto as the degree of influence.

Further, Japanese Patent No. 3226400 discusses the method of focusing onthe probabilities of the diagnosis names in the current inferenceresult, and using the sum of the change amounts of the probabilitieswhen not-entered information is added thereto as the degree ofinfluence. In the technique discussed in Japanese Patent No. 3226400, itis thereby possible to present not-entered information that has a highinfluence on the inference result of the apparatus based on enteredinformation.

However, the technique discussed in Japanese Patent No. 3226400 selectsnoteworthy not-entered information only based on possession of a highinfluence value on the inference result of the apparatus based onentered information. Therefore, sometimes, information (observation)less likely to exist in a medical image may be presented as noteworthynot-entered information.

SUMMARY OF THE INVENTION

The present invention is directed to a medical diagnostic supportingapparatus capable of efficiently presenting (e.g., displaying)information that a doctor should check preferentially.

According to an aspect of the present invention, a medical diagnosticsupporting apparatus, which provides information supporting a medicaldiagnosis, includes a medical image input unit configured to input amedical image that is targeted by the medical diagnosis, a medicalinformation acquisition unit configured to acquire one or more pieces ofmedical information with respect to the medical image as enteredinformation, an image feature amount acquisition unit configured toacquire an image feature amount from the medical image, a presentednot-entered information candidate selection unit configured to select aplurality of pieces of not-entered information associated with the imagefeature amount from not-entered information that is medical informationother than the entered information as presented not-entered informationcandidates that are candidates for presentation, an inference unitconfigured to acquire an inference result with use of the enteredinformation and each of the presented not-entered informationcandidates, a presented not-entered information selection unitconfigured to select presented not-entered information from thepresented not-entered information candidates based on the plurality ofinference result, and a presentation unit configured to present thepresented not-entered information selected by the presented not-enteredinformation selection unit.

Further features and aspects of the present invention will becomeapparent from the following detailed description of exemplaryembodiments with reference to the attached drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate exemplary embodiments, features,and aspects of the invention and, together with the description, serveto explain the principles of the invention.

FIG. 1 schematically illustrates an example of a configuration of amedical diagnostic supporting system including a medical diagnosticsupporting apparatus according to an exemplary embodiment of the presentinvention.

FIG. 2 is a flowchart illustrating an example of a processing procedureof a method for controlling the medical diagnostic supporting apparatusaccording to the exemplary embodiment of the present invention.

FIG. 3 illustrates a typical example of a probabilistic inference modelwith use of a Bayesian network according to the exemplary embodiment ofthe present invention.

FIG. 4 is a flowchart illustrating an example of a detailed processingprocedure performed in step S205 illustrated in FIG. 2.

FIG. 5 illustrates the exemplary embodiment of the present invention,and in particular, illustrates an example of the relationship betweenthe distance and angle between each point at the periphery and thecentroid of the area in the form of polar coordinate in which thevertical axis represents a distance r and the horizontal axis representsan angle θ.

FIG. 6 illustrates an example of a monitor display screen according tothe exemplary embodiment of the present invention.

DESCRIPTION OF THE EMBODIMENTS

Various exemplary embodiments, features, and aspects of the inventionwill be described in detail below with reference to the drawings.

FIG. 1 schematically illustrates an example of a configuration of amedical diagnostic supporting system including a medical diagnosticsupporting apparatus according to an exemplary embodiment of the presentinvention.

As illustrated in FIG. 1, a medical diagnostic supporting system 10includes a medical diagnostic supporting apparatus 100, a medical imagedatabase 200, a diagnostic record database 300, and a local area network(LAN) 400. In other words, the medical diagnostic supporting system 10illustrated in FIG. 1 is configured in such a manner that the medicaldiagnostic supporting apparatus 100 is connected to the medical imagedatabase 200 and the diagnostic record database 300 via the LAN 400.

The medical diagnostic supporting apparatus 100 is an apparatus forproviding information which supports a medical diagnosis by, forexample, a doctor. More specifically, the medical diagnostic supportingapparatus 100 is an apparatus for presenting noteworthy not-enteredinformation (that a doctor or the like should check preferentially) bymaking an inference regarding a medical diagnosis based on a pluralityof pieces of medical information that have been entered.

The medical diagnostic supporting apparatus 100 includes a control unit110, a monitor 120, a mouse 130, and a keyboard 140.

The control unit 110 controls the operation of the medical diagnosticsupporting apparatus 100. Control unit 110 includes a central processingunit (CPU) 111, a main memory 112, a magnetic disk 113 and a displaymemory 114, which are operatively interconnected to each other via a bus115. The CPU 111 executes a program stored in, for example, the mainmemory 112, which realizes various controls such as communication withthe medical image database 200 and the diagnostic record database 300,and overall control of the medical diagnostic supporting apparatus 100.

Mainly, the CPU 111 controls the operations of various constituentcomponents of the medical diagnostic supporting apparatus 100, andcomprehensively controls the operation of the medical diagnosticsupporting apparatus 100. The main memory 112 stores the control programto be executed by the CPU 111, and provides a work area when the CPU 111executes a program.

The magnetic disk 113 stores, for example, an operating system (OS),device drivers for peripheral devices, various kinds of applicationsoftware, and work data generated or used by, for example, the variouskinds of application software. The display memory 114 temporarily storesdisplay data to be displayed on the monitor 120.

The bus 115 communicably connects the various constituent components ofthe medical diagnostic supporting apparatus 100 to each other, andcommunicably connects the medical diagnostic supporting apparatus 100and the LAN 400 to each other.

The monitor 120 is constituted by, for example, a cathode-ray tube (CRT)monitor, a liquid crystal display (LCD) or the like that displays, forexample, an image based on the display data of the display memory 114according to the control of the CPU 111. Further, the execution statusand the execution result of a program executed by the CPU 111 aredisplayed on the monitor 120 when it is necessary.

The present exemplary embodiment will be described based on an exampleof presenting noteworthy not-entered information or the like on themonitor 120. That is, in the present exemplary embodiment noteworthynot-entered information or the like is displayed on the monitor 120.However, the present invention is not limited thereto. For example, inother embodiments, the not-entered information or the like may bepresented by being output to, for example, a printer, or may bepresented by being output by an audio means or another means. Suchembodiments are also within the scope of the present invention.

The mouse 130 and the keyboard 140 are members enabling a pointing inputand an input of a character and the like by a user, respectively. A user(e.g., a doctor) inputs various commands (instruction/order) to themedical diagnostic supporting apparatus 100 by operating the mouse 130and the keyboard 140.

The medical diagnostic supporting apparatus 100 according to the presentexemplary embodiment can read out various kinds of image data from themedical image database 200 through the LAN 400, and can readout variouskinds of diagnostic record data from the diagnostic record database 300.

As the medical image database 200, an existing Picture Archiving andCommunication System (PACS) can be used. As the diagnostic recorddatabase 300, an electronic health record system, which is a subsystemof an existing Hospital Information System (HIS), can be used.

Alternatively, an external storage apparatus such as a floppy disk drive(FDD), a hard disk drive (HDD), a compact disk (CD) drive, a digitalversatile disk (DVD) drive, a magnetooptic disk (MO) drive, or a ZIPdrive may be connected to the medical diagnostic supporting apparatus100, and the medical image data and the diagnostic record data may beread from the connected apparatus.

The medical image database 200 stores, for example, various kinds ofmedical image data captured by a medical image capturing apparatus (notshown). The medical diagnostic supporting apparatus 100 is connected tothe medical image database 200 via the LAN 400, and acquires necessarymedical image data therefrom. Examples of kinds of medical image for usein the present exemplary embodiment include a plain X-ray image(roentgen image), an X-ray CT image, an MRI image, a positron emissiontomography (PET) image, a single photon emission computed tomography(SPECT) image, and an ultrasonic image.

The diagnostic record database 300 stores various kinds of diagnosticrecord data. The medical diagnostic supporting apparatus 100 isconnected to the diagnostic record database 300 via the LAN 400, andacquires necessary diagnostic record data therefrom.

Various kinds of information are written in the diagnostic record foruse in the present exemplary embodiment. The information includespersonal information (for example, name, birth date, age, and sex) andclinical information (for example, various examination values, a chiefcompliant, previous medical history, and treatment history) of apatient, reference information to the medical image data of the patientstored in the medical image database 200, and information about anobservation by a primary doctor. Further, when the diagnosis advances, adiagnosed name is written in this diagnostic record.

The LAN 400 communicably connects the medical diagnostic supportingapparatus 100, and the medical image database 200 and the diagnosticrecord database 300.

Next, a processing procedure of a method for controlling the medicaldiagnostic supporting apparatus 100 according to the present exemplaryembodiment will be described.

FIG. 2 is a flowchart illustrating an example of the processingprocedure of the method for controlling the medical diagnosticsupporting apparatus 100 according to the exemplary embodiment of thepresent invention. In particular, the CPU 111 executes the programstored in the main memory 112, which realizes the flowchart illustratedin FIG. 2.

Further, FIG. 3 illustrates an example of a probabilistic inferencemodel with use of a Bayesian network according to the present exemplaryembodiment.

In the following description, a case where supporting of a medicaldiagnosis of an abnormal shadow in a lung is performed by using themedical diagnostic supporting apparatus 100 is described as an example.It should be noted that an inference target is not limited thereto, andoptions of diagnosis name and enterable medical information thereforwhich will be described below are all just an example for facilitatingunderstanding of the steps of the processing by the medical diagnosticsupporting apparatus 100.

Further, in the following description, options for diagnosis name (typesof abnormality) handled by the medical diagnostic supporting apparatus100 will be denoted with use of character “D”. More specifically, thepresent exemplary embodiment handles “primary lung cancer”, “metastasisof cancer to lung”, and “another abnormality” illustrated in a state 303in FIG. 3 as options for diagnosis name, which will be denoted as “D₁”,“D₂”, and “D₃”, respectively in the following description.

Further, in the following description, the medical information handledby the medical diagnostic supporting apparatus 100 will be denoted as“Ij”. More specifically, the present exemplary embodiment handles seventypes of medical information I₁ to I₂ illustrated in FIG. 3. Forexample, medical information I₁ “shape” illustrated in FIG. 3 indicateswhat kind of shape an abnormal shadow has. Further, medical informationI₅ “pleural indentation/involvement” illustrated in FIG. 3 indicateswhether there is pleural indentation/involvement in the organ.

Further, in the following description, possible states (discrete values)for each medical information “Ij” will be denoted as “Sjk”. The range ofk varies depending on “Ij”. In the present exemplary embodiment, asillustrated in FIG. 3, for example, medical information I₁ “shape” hasthree possible states, S₁₁ “shape—spherical”, S₁₂ “shape—lobulated”, andS₁₃ “shape—irregular”.

Further, in the following description, a group of “Ij” will be denotedwith use of character “N”, and a group of “Sjk” will be denoted with useof character “C” or “E”.

Character “C” is used to denote a plain group of “Sjk”. For example, agroup constituted by S₁₁, S₁₂, and S₅₁ as elements is expressed asC={S₁₁, S₁₂, S₅₁}. In the example illustrated in FIG. 3, this group isC={shape—spherical, shape—lobulated, pleuralindentation/involvement—yes}.

On the other hand, character “E” is used to denote a settable group of“Sjk”. The term “settable” here means that “Sjk” cannot existsimultaneously for each “Ij” in a single “E”. Similar to theabove-described example, if there are S₁₁, S₁₂, and S₅₁ as elements,{S₁₁, S₅₁} can occur, but {S₁₁, S₁₂} never occurs. This is because onetype of medical information can have only one state. In the exampleillustrated in FIG. 3, the group {shape—spherical, pleuralindentation/involvement—yes} can occur, but the group {shape—spherical,shape—lobulated} never occurs.

First, in step S201 in FIG. 2, the CPU 111 performs input processing ofdesired medical image data from the medical image database 200 to themedical diagnostic supporting apparatus 100 in response to an input tothe mouse 130 and the keyboard 140 (medical image input process).

Hereinafter, the medical image data input in step S201 will be referredto as “interpretation target image”. As mentioned above, thisinterpretation target image data input processing is carried out by theCPU 111 receiving desired medical image data as an interpretation targetimage through the LAN 400 from the medical image database 200 storingcaptured medical image data.

The processing of step S201 may be realized by the CPU 111 reading outdesired medical image data as an interpretation target image from anystorage medium connected to the medical diagnostic supporting apparatus100, such as a FDD, a CD-rewritable (CD-RW) drive, an MO(magneto-optical) drive, and a ZIP drive.

Next, in step S202, the CPU 111 displays, on the monitor 120, theinterpretation target image input into the medical diagnostic supportingapparatus 100 in step S201.

In step S203, the CPU 111 acquires, as medical information, anobservation from shadow interpretation that a doctor inputs with userof, for example, the mouse 130 and the keyboard 140 while viewing theinterpretation target image displayed on the monitor 120 (medicalinformation acquisition process).

This processing of step S203 is realized by, for example, providing themedical diagnostic supporting apparatus 100 with such a function that adoctor can select any one of the corresponding states Sjk for therespective types of medical information Ij by using a template-basedinterpretation observation input supporting method.

In the following description, a group of medical information Ij acquiredin step S203 will be denoted as “Nf”, and a group of states Sjk thereofwill be denoted as “Ef”. These pieces of information will be referred toas “entered information”.

For example, it is assumed that the medical information acquired in stepS203 is S₁₁ “shape—spherical”, S₃₃ “radial shape—none”, S₄₁ “bronchialindentation/involvement—yes”, S₅₃ “pleural indentation/involvement—yes”,and S₇₁ “size—small” illustrated in FIG. 3. In this case, {I₁, I₃, I₄,I₅, I₇} is acquired as the entered information Nf, and {S₁₁, S₃₃, S₄₁,S₅₃, S₇₁} is acquired as the entered information Ef.

If there are a plurality of tumor masses in the interpretation targetimage, a doctor should identify which tumor mass the doctor interprets(which tumor mass the doctor inputs an observation for) by some method.

For example, this can be realized by the doctor specifying the locationof the tumor mass by using the mouse 130 on the interpretation targetimage displayed on the monitor 120. The location of the tumor mass maybe specified by, for example, clicking the center of the tumor mass orspecifying a rectangular area surrounding the tumor mass.

Next, in step S204, the CPU 111 performs image processing on theinterpretation target image input in step S201, and acquires variousimage feature amounts regarding the tumor mass that the doctor hasspecified as an interpretation target by, for example, the mouse 130 instep S203 (image feature amount acquisition process).

More specifically, in the processing of step S204, the followinginformation is acquired as image feature amounts. First, the area of thetumor mass specified as the interpretation target is extracted bybinarization processing, and the degree of irregularity of the peripheryis calculated based on the degree of coincidence of the area obtained byapplying distance conversion to the area and inversely converting onlypixels having the maximum value (for the details thereof, refer to, forexample, “Classification of tumors in chest X-ray CT images into thesolid and air-containing type and its application to discrimination ofthe benign and malignant tumors” written by Kondo, et al, published inthe Institute of Electronics, Information and Communication Engineers(IEICE) technical report MI2000-16, May 2000).

Further, after the area of the tumor mass specified as theinterpretation target is extracted, the distances from the respectivepoints on the periphery to the centroid of the area, and the variancethereof are calculated. Further, the bronchus is extracted with use of,for example, the method described in “Bronchial wall regions extractionalgorithm using multi slice CT images” written by Tada, et al, publishedin the IEICE technical report MI2004-37, September 2004, and it isdetermined whether the bronchus exists within the tumor mass. Further,if it is determined that the bronchus exists in the tumor mass, it isdetermined whether there is an annular structure of air based on theinternal density of the tumor mass. In addition, the maximum diameter ofthe tumor mass is calculated.

It should be noted that these image feature amounts are mere an examplefor facilitating understanding of image feature amount in the presentexemplary embodiment, and image feature amounts to be acquired are notlimited thereto.

Further, since the image feature amounts acquired in step S204 are usedto narrow down not-entered information, only image feature amountshaving an influence on not-entered information may be acquired. In otherwords, the processing for acquiring image feature amounts having aninfluence only on the entered information Nf acquired in step S203 maybe omitted. For example, when S₇₁ “size—small” illustrated in FIG. 3 isacquired as entered information, calculation of the maximum diameter ofthe tumor mass may be omitted.

Next, in step S205, the CPU 111 selects not-entered information to bepresented in step S206 (hereinafter referred to as “presentednot-entered information”) based on the medical information on theinterpretation target image acquired in step S203 and the image featureamounts acquired in step S204.

In the present exemplary embodiment, the diagnosis name having thehighest probability is selected as a predicted diagnosis name Df fromthe inference result based on the entered information Ef. Not-enteredinformation Eh, which is the not-entered information most affirming thepredicted diagnosis name Df, and not-entered information El, which isthe not-entered information most denying the predicted diagnosis nameDf, are estimated, and selected as presented not-entered information.

Now, the processing procedure performed in step S205 illustrated in FIG.2 will be described in detail. FIG. 4 is a flowchart illustrating anexample of the detailed processing procedure performed in step S205illustrated in FIG. 2.

Upon a start of the processing of step S205 illustrated in FIG. 2,first, in step S401 in FIG. 4, the CPU 111 calculates and acquires theprobability P (Di|Ef) belonging to each diagnosis name Di (i=1 to 3)based on the entered information Ef as an input with use of theprobabilistic inference model.

At this time, the CPU 111 stores the Di having the highest probabilityin the probabilities P (Di|Ef) as the predicted diagnosis name Df intothe main memory 112, and also stores this probability P (Di|Ef)(hereinafter denoted as “Pf”) into the main memory 112.

In the following description, an example the case is described where thehighest probability Pf=66.2% is acquired for the diagnosis name D₂“metastasis of cancer to lung” in an inference based on the enteredinformation Ef as an input. This calculation of the inferred probabilityPf corresponds to a second inference process in the present exemplaryembodiment, and the acquired inferred probability Pf corresponds to asecond inference result in the present exemplary embodiment.

There are various methods as an inference model for performing theinference processing of calculating the inferred probability belongingto a currently focused diagnosis name with use of entered information.As an example thereof, the present exemplary embodiment employs theBayesian network as illustrated in FIG. 3. It should be noted that thefollowing method is mere an example of the inference model, and theinference method, the structure of the inference model, and the like arenot limited thereto.

The Bayesian network illustrated in FIG. 3 is a model expressing aphenomenon by a causal relationship between a plurality of events. Inthe Bayesian network illustrated in FIG. 3, a relationship betweenevents is expressed by a probability, and an event constituting thetarget phenomenon is expressed as a node 301, and a relationship betweennodes is expressed as a link 302.

As illustrated in FIG. 3, the links 302 are indicated by arrows. Thenode at the roots of the arrows is referred to as “parent node”, and thenodes at the tips of the arrows are referred to as “child nodes”. Eachnode has a plurality of states 303 indicating states of the node. Anoccurrence probability (referred to as “prior probability”) is assignedto each state. The relationship between the parent node and the childnode is determined based on a conditional probability in which theparent node is set as a condition. The table containing this conditionalprobability is referred to as a conditional probability table (CPT).

The CPT 304 illustrated in FIG. 3 is an example of the CPT between theparent node 301 and the node I₇ which is a child node. Further,information indicating what states (for example, state 303) of at leastone node of a target model are, is referred to as “evidence”. Theprobability (referred to as “posterior probability”) of a desired nodecan be acquired based on this evidence, the CPT 304, and the Bayes'theorem by the belief propagation method.

Returning to FIG. 4, upon a completion of the processing of step S401,the processing proceeds to step S402. In step S402, the CPU 111 acquiresa group of the states belonging to all pieces of enterable medicalinformation except for the entered information Nf (other than theentered information), i.e., a group (hereinafter denoted as “Cv”) of thestates of the not-entered medical information.

For example, in the example illustrated in FIG. 3, if the enteredinformation Nf is {I₁, I₃, I₄, I₅, I₇}, a group constituted by thestates of medical information I₂ and I₆ {S₂₁, S₂₂, S₂₃, S₆₁, S₆₂, S₆₃}is acquired as “Cv”.

Next, in step S403, the CPU 111 acquires a group (hereinafter denoted as“Cif”) constituted by the states of the medical informationcorresponding to the image feature amounts based on the image featureamounts acquired in step S204.

For example, the state of I₁ “shape” illustrated in FIG. 3 is selectedwith use of the degree of irregularity of the periphery which is one ofthe image feature amounts. For example, if it is determined that thedegree of irregularity of the periphery is high (equal to or higher thana predetermined threshold value), S₁₂ {shape—lobulated} and S₁₃{shape—irregular} are selected to be included in the elements of “Cif”as possible corresponding states. On the contrary, if it is determinedthat the degree of irregularity of the periphery is low (equal to orlower than the predetermined threshold value), S₁₁ {shape—spherical} isselected to be included in the elements of “Cif” as a possiblecorresponding state.

Further, the state of I₂ “notch” is selected based on the distance ofthe periphery which is one of the image feature amounts. In thefollowing, an example of the selection method therefor will be describedwith reference to FIG. 5.

FIG. 5 illustrates an example of the relationship between the distanceand the angle between each point at the periphery and the centroid ofthe area in the form of polar coordinate in which the vertical axisrepresents a distance r and the horizontal axis represents an angle θ,according to the present exemplary embodiment.

First, a centroid point G (white circle in FIG. 5) of each area 511 to531, and a peripheral point Ma (black circle in FIG. 5) which has thelongest distance (r0) from the centroid point G are acquired. Then, thedirection (hereinafter referred to as “reference direction”) of the linesegment connecting these two points is acquired.

At this time, an arbitrary peripheral point can be expressed by thedistance r from the centroid point G, and the angle θ defined betweenthe line segment connecting the centroid point G and the peripheralpoint and the reference direction (in the clockwise direction).

Then, an angular range θsk (k>=1) of points having the distance r fromthe centroid point G which is equal to or shorter than a threshold valuerth is acquired. Further, the minimum distance r (hereinafter referredto as “rmink”) within the range θsk is acquired. If θsk is within apredetermined range and acquired once or twice, it is determined thatthe possibility of presence of a notch is high (although it is unclearhow deep or shallow the notch is).

In the example (511 and 512) illustrated at the upper portion in FIG. 5,θsk are acquired twice, but neither of the θsk are within thepredetermined range (not shown), and therefore it is determined that thepossibility of presence of a notch is low. Then, S₂₂ {notch—shallow} andS₂₃ {notch—none} illustrated in FIG. 3 are selected to be included inthe elements of “Cif” as possible corresponding states.

On the other hand, in the example (521 and 522) illustrated at themiddle in FIG. 5, θsk is acquired once and this θsk is within thepredetermined range (not shown), and therefore it is determined that thepossibility of presence of a notch is high. Then, S₂₁ {notch—deep} andS₂₂ {notch—shallow} illustrated in FIG. 3 are selected to be included inthe elements of “Cif” as possible corresponding states.

On the other hand, in the example (531 and 532) illustrated at thebottom in FIG. 5, θsk are acquired six times and therefore it isdetermined that the possibility of presence of a notch is low. Then, S₂₂{notch—shallow} and S₂₃ {notch—none} illustrated in FIG. 3 are selectedto be included in the elements of “Cif” as possible correspondingstates.

Further, the state of I₃ “radial shape” illustrated in FIG. 3 isselected based on the variance of the distances at the periphery whichis one of the image feature amounts. For example, if it is determinedthat the variance is large (equal to or larger than a predeterminedthreshold value), S₃₁ {radial shape—highly radial} and S₃₂ {radialshape—slightly radial} illustrated in FIG. 3 are selected to be includedin the elements of “Cif” as possible corresponding states.

On the contrary, if it is determined that the variance is small (equalto or smaller than the predetermined threshold value), S₃₂ {radialshape—slightly radial} and S₃₃ {radial shape—none} illustrated in FIG. 3are selected to be included in the elements “Cif” as possiblecorresponding states.

Similarly, the state of I₆ “air bronchogram” illustrated in FIG. 3 isselected (determined) based on information indicating whether thebronchus exists within the tumor mass which is one of the image featureamounts.

For example, if it is determined that the bronchus does not exist withinthe tumor mass, all of the states regarding air bronchogram are excludedfrom the elements of “Cif”. For example, this is applicable to the casewhere a tumor mass exists at the side of a main bronchus or near a chestwall. This is because the determination is impossible withoutinformation with respect to air bronchogram in the image.

It should be noted that the state S₆₃ {air bronchogram—none} indicatesthat there is no air bronchogram when the bronchus exists within thetumor mass, and therefore indicates a different state from theabove-described case.

On the other hand, if it is determined that the bronchus exists withinthe tumor mass, the state of I₆ “air bronchogram” illustrated in FIG. 3is further determined based on the information indicating whether thereis an annular structure of air within the tumor mass which is one of theimage feature amounts. For example, if there is an annular structure ofair within the tumor mass, the state S₆₁ {air bronchogram—yes} isselected to be included in the elements of “Cif” as a possiblecorresponding state.

On the other hand, if there is no annular structure of air within thetumor mass, since the determination is difficult for this case, all ofthe states S₆₁ {air bronchogram—yes}, S₆₂ {air bronchogram—unknown}, andS₆₃ {air bronchogram—none} are selected to be included in the elementsof “Cif” as possible corresponding states.

Further, the state of I₇ “size” illustrated in FIG. 3 is selected(determined) based on the size of the tumor mass which is one of theimage feature amounts.

If the acquired size of the tumor mass is smaller than a predeterminedthreshold value, the states S₇₁ {size—small} and S₇₂ {size—medium} areselected to be included in the elements of “Cif” as possiblecorresponding states. On the contrary, if the acquired size of the tumormass is larger than the predetermined threshold value, the states S₇₃{size—large} and S₇₂ {size—medium} are selected to be included in theelements of “Cif” as possible corresponding states.

Regarding the medical information that cannot be determined from theimage feature amounts, all of the states thereof are selected to beincluded in the elements “Cif”.

It should be understood that the above description is mere an exampleindicating the correspondence between an image feature amount and astate of medical information, and image feature amounts to be used, amethod for selecting a state and the like are not limited thereto. Forexample, only a part of the above-described determination processing maybe used.

For example, when it is determined that the bronchus does not existwithin the tumor mass, only the processing of excluding all of thestates regarding I₆ “air bronchogram” illustrated in FIG. 3 from “Cif”may be performed. Further, any image feature amount other than the imagefeature amounts described above may be used, and any determinationprocessing for determining the existence possibility of a correspondingstate from the image feature amount may be used.

For example, it is assumed that the following image feature amounts areacquired from the processing of step S204.

* degree of irregularity of periphery: equal to or smaller than thepredetermined threshold value* shape of distances of periphery: no θsk is acquired* variance of distances of periphery: equal to or smaller than thepredetermined threshold value* the bronchus exists in the tumor mass* there is an annular structure of air within the tumor mass* size of tumor mass: equal to or smaller than the predetermined value

In this case, in step S403, {S₁₁, S₂₂, S₂₃, S₃₂, S₃₃, S₄₁, S₄₂, S₄₃,S₅₁, S₅₂, S₅₃, S₆₁, S₇₁, S₇₂} is set as “Cif”.

Returning to FIG. 4, upon a completion of the processing of step S403,the processing proceeds to step S404. In step S404, the CPU 111determines common states existing in both “Cv” acquired in step S402 and“Cif” acquired in step S403, and acquires a group (hereinafter denotedas “Cuf”) constituted by possible states of not-entered medicalinformation.

In the above-described example, comparison of “Cv” and “Cif” todetermine common states shared thereby results in the acquisition of{S₂₂ “notch—shallow”, S₂₃ “notch—none”, S₆₁ “air bronchogram—yes”} as“Cuf”. In other words, S₂₁ “notch—deep”, S₆₂ “air bronchogram—unknown”,and S₆₃ “air bronchogram—none” each are excluded from candidates forpresentation as states unlikely to exist.

Next, in step S405, the CPU 111 prepares a variable m, and sets 1 to m(m=1).

Next, in step S406, the CPU 111 first selects one or more “Sjk” from“Cuf” which is a group constituted by candidates of states ofnot-entered medical information. Then, the CPU 111 generates (acquires)a virtual group Em of “Sjk”, and sets this group as a presentednot-entered information candidate (presented not-entered informationcandidate selection process).

In the case of m>1 (in other words, in the case of the second executionof the processing of step S406 or the execution of the processing ofstep S406 after that), the CPU 111 generates (acquires), as Em, a groupdifferent from the groups E₁ to E_(m-1) that have been generated beforethis time. The number of “Sjk” belonging to Em may be a predeterminedplural number or a number less than the plural number, or may bespecified by a doctor.

In the following description, “1” is set as the number of “Sjk” as anexample. For example, in the above-described example, “Cuf” isconstituted by S₂₂ “notch—shallow”, S₂₃ “notch—none”, and S₆₃ “airbronchogram—yes” illustrated in FIG. 3. In this case, for example, theCPU 111 generates (acquires) E₁ as {S₂₂ “notch—shallow”}.

In step S407, the CPU 111 inputs the set of the entered information Efand the presented not-entered information candidate Em, and calculatesthe probability P (Df|Ef, Em) belonging to the predicted diagnosis nameDf by the inference model. Then, the CPU 111 stores the acquiredinferred probability (hereinafter denoted as “Pm”) in the main memory112 in such a manner that this inferred probability is associated withthe presented not-entered information candidate Em.

This calculation of the inferred probability Pm corresponds to aninference process in the present exemplary embodiment, and the acquiredinferred probability Pm corresponds to an inference result in thepresent exemplary embodiment. For example, if the predicted diagnosisname Df is “metastasis of cancer to lung”, the inferred probabilitybelonging to “metastasis of cancer to lung” is calculated by theinference model illustrated in FIG. 3 with use of the set of Ef={S₁₁,S₃₃, S₄₁, S₅₃, S₇₁} and E₁={S₂₂} as an input, as a result which is60.2%. Then, this inferred probability is stored in such a manner thatthis inferred probability is associated with E₁={S₂₂}.

Next, in step S408, the CPU 111 adds 1 to the variable m.

Next, in step S409, the CPU 111 determines whether the processing fromthe step S406 to step S408 should be repeated. More specifically, instep S409, the CPU 111 determines whether the value of the valuable m isgreater than the number (hereinafter denoted as “CE”) of possiblecombinations as Em.

If it is determined as a result of the determination in step S409 thatthe value of the valuable m is not greater than CE (in other words, thevalue of the valuable m is smaller than CE) (NO in step S409), sincethis means that acquisition of all Pm is not yet completed, theprocessing returns to step S406 so that the processing of step S406 andthe subsequent processing are repeated.

On the other hand, if it is determined as a result of the determinationof step S409 that the value of the valuable m is greater than CE (YES instep S409), the processing proceeds to step S410. For example, in theabove-described example, since the number of possible combinations ofEm, i.e., CE is 3, the processing from step S406 to step S409 isrepeated until E₃ is processed. Then, when m becomes 4, since m becomesgreater than CE, the processing proceeds to step S410.

In step S410, the CPU 111 selects any of Em generated and acquired instep S406 as not-entered information that is most worthy of beingpresented to a doctor (presented not-entered information selectionprocess).

More specifically, Em having the highest inferred probability Pm storedin the main memory 112 by the above-described processing is stored inthe main memory 112 as not-entered information Eh that most affirms thepredicted diagnosis name.

Further, the inferred probability (hereinafter denoted as “Ph”) at thistime is stored in the main memory 112 in such a manner that this isassociated with the not-entered information Eh. Similarly, Em having thelowest inferred probability Pm is stored in the main memory 112 asnot-entered information El that most denies the predicted diagnosisname. Further, the inferred probability (hereinafter denoted as “Pl”) atthis time is stored in the main memory 112 in such a manner that this isassociated with the not-entered information El.

TABLE 1 m E_(m) P (D₂|E_(f), E_(m)) 1 {NOTCH - SHALLOW} 60.2% 2 {NOTCH -NONE} 71.6% 3 {AIR BRONCHOGRAM - YES} 44.1% P (D₂|E_(f)) 66.2%

The table 1 indicates an example of E₁ to E₃ and the inferredprobability P (Df|Ef, Em) thereof in the above-described example. Inthis example, E₂, i.e., S₂₃ {notch—none} illustrated in FIG. 3 providesthe highest inferred probability 71.6%, while E₃, i.e., S₆₁ {airbronchogram—yes} illustrated in FIG. 3 provide the lowest inferredprobability 44.1%. Therefore, in this case, Eh={S₂₃} and El={S₆₁} arestored in the main memory 112.

After the processing of step S410 is completed, the operation accordingto the flowchart of FIG. 4 is ended. In this way, execution ofprocessing of steps S401 to S410 illustrated in FIG. 4 constitutesexecution of the processing of step S205 illustrated in FIG. 2.

Now, returning to FIG. 2, after the processing of step S205 iscompleted, the processing proceeds to step S206. In step S206, the CPU111 displays and presents, for example, the noteworthy not-enteredinformation (for example, presented not-entered information) selected instep S205 on the monitor 120 (presentation process). More specifically,the CPU 111 displays Eh (not-entered information affirming Df) and El(not-entered information denying Df) stored in the main memory 112 onthe monitor 120.

FIG. 6 illustrates an example of a monitor display screen according tothe present exemplary embodiment. In the example illustrated in FIG. 6,the monitor display screen 600 shows the interpretation target image 610displayed in step S202.

Further, the monitor display screen 600 shows the predicted diagnosisname Df (metastasis of cancer to lung) acquired in step S401 at apredicted diagnosis name 620 field, the medical information (enteredinformation) Ef acquired in step S203 at an entered information 630field. Further, the monitor display screen 600 shows noteworthynot-entered information 640.

More specifically, the not-entered information Eh that affirms thepredicted diagnosis name, i.e., “notch—none” is displayed as noteworthynot-entered information 641, and the not-entered information El thatdenies the predicted diagnosis name, i.e., “air bronchogram—yes” isdisplayed as noteworthy not-entered information 642.

Use of the medical diagnostic supporting apparatus 100 according to thepresent exemplary embodiment enables a doctor to recognize medicalinformation that a doctor should check preferentially to obtaininformation helpful for his/her diagnosis by referring to the presentednot-entered information.

The medical diagnostic supporting apparatus 100 according to the presentexemplary embodiment can present not-entered information that likelyexists in an interpretation target image and has an influence on apredicted diagnosis name. Due to this function, the medical diagnosticsupporting apparatus 100 can present preferential medical informationhelpful for a diagnosis to, for example, a doctor.

Further, the medical diagnostic supporting apparatus 100 according tothe present exemplary embodiment uses image feature amounts only for thepurpose of excluding obviously irrelevant states from candidates forstates to be presented. Therefore, unlike the processing ofautomatically determining an observation from image feature amounts, itis possible to realize a diagnosis support effectively using informationof image feature amounts, even when it is difficult to obtain perfectaccuracy of detecting the image feature amounts and reliably associatethe image feature amount with states.

As mentioned above, according to the medical diagnostic supportingapparatus 100 according to the present exemplary embodiment, it ispossible to efficiently present information that a doctor should checkpreferentially, due to selection and presentation of not-enteredinformation that has a high influence on an inference result and ishighly likely to exist.

In the above-described exemplary embodiment, the medical diagnosticsupporting apparatus 100 acquires, as medical information, anobservation that a doctor inputs as a result of his/her interpretationof a medical image that the medical diagnostic supporting apparatus 100displays in step S203 in FIG. 2. However, in the present invention, themethod for acquiring medical information is not limited thereto. Forexample, the medical diagnostic supporting apparatus 100 may set, as aprocessing target (entered information/not-entered information), medicalinformation of medical examination data including, for example, aprevious interpretation report, a clinical report, and other informationusable for diagnosis supporting processing with respect to a person(patient) to be examined.

Further, the processing of acquiring image feature amounts performed instep S204 in FIG. 2 may be performed before step S203 in FIG. 2 or afterstep S402 in FIG. 2.

In the above-described exemplary embodiment, the description has beengiven assuming that all medical information has discrete values.However, instead, continuous values may be acquired as input informationand these values may be discretized to be used.

Further, in the above-described exemplary embodiment, the medicaldiagnostic supporting apparatus 100 selects, as the elements of “Cif”,all of the states with respect to the observations that have not beendetermined as to their possibilities of presence or absence based on theimage feature amounts in the processing of step S403 in FIG. 4. However,the present invention is not limited thereto, and the medical diagnosticsupporting apparatus 100 may be configured in such a manner that all ofsuch states are excluded from “Cif”.

Further, the presented not-entered information selected in step S205 inFIG. 2 is selected based on the diagnosis name that has the highestprobability in the probabilities acquired in step S401 in FIG. 4.However, the present invention is not limited thereto.

For example, the medical diagnostic supporting apparatus 100 may beconfigured in such a manner that not-entered information that providesthe highest (or lowest) possibility for the diagnosis name is selectedfor each diagnosis name (D₁, D₂, and D₃), and each of that not-enteredinformation is presented while being associated with the correspondingdiagnosis name.

Further, the present invention can be also embodied by performing thefollowing procedure. That is, a software application (or program)capable of carrying out the functions of the above-described exemplaryembodiment is supplied to a system or an apparatus through a network orvarious kinds of storage media, and a computer (or, for example, a CPUor a micro processing unit (MPU)) of the system or the apparatus readsout and executes the program.

While the present invention has been described with reference toexemplary embodiments, it is to be understood that the invention is notlimited to the disclosed exemplary embodiments. The scope of thefollowing claims is to be accorded the broadest interpretation so as toencompass all modifications, equivalent structures, and functions.

This application claims priority from Japanese Patent Application No.2009-295647 filed Dec. 25, 2009, which is hereby incorporated byreference herein in its entirety.

1. A medical diagnostic supporting apparatus for providing informationwhich supports a medical diagnosis, the medical diagnostic supportingapparatus comprising: a medical image input unit configured to input amedical image that is a target of the medical diagnosis; a medicalinformation acquisition unit configured to acquire one or more pieces ofmedical information relating to the medical image as enteredinformation; an image feature amount acquisition unit configured toacquire an image feature amount from the medical image; a presentednot-entered information candidate selection unit configured to select aplurality of pieces of not-entered information associated with the imagefeature amount from not-entered information that is medical informationother than the entered information as presented not-entered informationcandidates that are candidates for presentation; an inference unitconfigured to acquire an inference result using the entered informationand each of the presented not-entered information candidates; apresented not-entered information selection unit configured to selectpresented not-entered information from the presented not-enteredinformation candidates based on the plurality of inference results; anda presentation unit configured to present the presented not-enteredinformation selected by the presented not-entered information selectionunit.
 2. The medical diagnostic supporting apparatus according to claim1, wherein the presented not-entered information selected by thepresented not-entered information selection unit includes not-enteredinformation that affirms a predicted diagnosis name.
 3. The medicaldiagnostic supporting apparatus according to claim 2, wherein thepresented not-entered information selection unit selects the presentednot-entered information from the presented not-entered informationcandidates based on the plurality of inference results such that thenot-entered information that affirms the predicted diagnosis name hasthe highest inferred probability out of the plurality of inferenceresults.
 4. The medical diagnostic supporting apparatus according toclaim 1, wherein the presented not-entered information selected by thepresented not-entered information selection unit includes not-enteredinformation that denies a predicted diagnosis name.
 5. The medicaldiagnostic supporting apparatus according to claim 1, wherein thepresented not-entered information selection unit selects the presentednot-entered information from the presented not-entered informationcandidates based on the plurality of inference results such that thenot-entered information that denies the predicted diagnosis name has thelowest inferred probability out of the plurality of inference results.